Monitoring a condition of a subject

ABSTRACT

Apparatus for monitoring a subject is described. A sensor monitors the subject during a sleeping session of the subject, and generates a sensor signal in response thereto. A control unit analyzes the sensor signal. In response to analyzing the sensor signal, the control unit performs an action selected from the group consisting of: identifying that the subject is currently undergoing an apnea episode, and predicting that the subject is going to undergo an apnea episode. In response thereto, the control unit changes a position of at least a portion of a body of the subject by activating a device. Other applications are also described.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. Ser. No. 14/810,814 toShinar (published as US 2015/0327792), filed Jul. 28, 2015, which is:

(A) a continuation of U.S. patent application Ser. No. 14/663,835 toShinar (published as US 2015/0190087), filed Mar. 20, 2015,

-   -   which is a continuation-in-part of U.S. patent application Ser.        No. 14/557,654 to Halperin (issued as U.S. Pat. No. 9,026,199),        filed Dec. 2, 2014,    -   which is a continuation of U.S. patent application Ser. No.        14/454,300 to Halperin (issued as U.S. Pat. No. 8,942,779),        filed Aug. 7, 2014,    -   which is a continuation of U.S. patent application Ser. No.        14/150,115 to Pinhas (issued as U.S. Pat. No. 8,840,564), filed        Jan. 8, 2014, which is:        -   (i) a continuation of U.S. patent application Ser. No.            13/921,915 to Shinar (issued as U.S. Pat. No. 8,679,030),            filed Jun. 19, 2013,        -   which is a continuation of U.S. patent application Ser. No.            13/107,772 to Shinar (issued as U.S. Pat. No. 8,491,492),            filed May 13, 2011, which:            -   is a continuation-in-part of U.S. patent application                Ser. No. 11/782,750 to Halperin (issued as U.S. Pat. No.                8,403,865), filed Jul. 25, 2007; and            -   is a continuation-in-part of U.S. patent application                Ser. No. 11/552,872 to Pinhas (published as US                2007/0118054), filed Oct. 25, 2006, now abandoned, which                claims the benefit of:                -   (a) U.S. Provisional Patent Application 60/731,934                    to Halperin, filed Nov. 1, 2005,                -   (b) U.S. Provisional Patent Application 60/784,799                    to Halperin filed Mar. 23, 2006, and                -   (c) U.S. Provisional Patent Application 60/843,672                    to Halperin, filed Sep. 12, 2006; and            -   (ii) a continuation-in-part of U.S. patent application                Ser. No. 13/863,293 to Lange, filed Apr. 15, 2013,                published as US 2013/0245502, now abandoned,            -   which is a continuation of U.S. patent application Ser.                No. 11/552,872 to Pinhas (published as US 2007/0118054),                filed Oct. 25, 2006, now abandoned, which claims the                benefit of                -   (a) U.S. Provisional Patent Application 60/731,934                    to Halperin, filed Nov. 1, 2005,                -   (b) U.S. Provisional Patent Application 60/784,799                    to Halperin filed Mar. 23, 2006, and                -   (c) U.S. Provisional Patent Application 60/843,672                    to Halperin, filed Sep. 12, 2006,

all of the above applications being incorporated herein by reference;and

(B) a continuation-in-part of U.S. patent application Ser. No.14/624,904 to Halperin, filed Feb. 18, 2015 (published as US2015/0164433),

-   -   which is a continuation of U.S. patent application Ser. No.        14/231,855 (issued as U.S. Pat. No. 8,992,434 to Halperin),    -   which is a continuation of U.S. patent application Ser. No.        14/020,574 (issued as U.S. Pat. No. 8,731,646 to Halperin),        filed Sep. 6, 2013,    -   which is a divisional of U.S. patent application Ser. No.        13/750,962 (issued as U.S. Pat. No. 8,679,034 to Halperin),        filed Jan. 25, 2013,    -   which is a continuation of U.S. patent application Ser. No.        11/782,750 (issued as U.S. Pat. No. 8,403,865 to Halperin),        filed Jul. 25, 2007,    -   which is a continuation-in-part of:    -   (i) U.S. patent application Ser. No. 11/197,786 (issued as U.S.        Pat. No. 7,314,451 to Halperin), filed Aug. 3, 2005, which        claims the benefit of:        -   (a) U.S. Provisional Patent Application 60/674,382, filed            Apr. 25, 2005, and        -   (b) U.S. Provisional Patent Application 60/692,105, filed            Jun. 21, 2005, and    -   (ii) U.S. patent application Ser. No. 11/446,281 (issued as U.S.        Pat. No. 8,376,954 to Lange), filed Jun. 2, 2006,    -   which is a continuation of U.S. patent application Ser. No.        11/048,100, filed Jan. 31, 2005, which issued as U.S. Pat. No.        7,077,810,    -   which claims the benefit of U.S. Provisional Patent Application        60/541,779, filed Feb. 5, 2004.

FIELD OF THE INVENTION

Some applications of the present invention relate generally topredicting and monitoring physiological conditions. Specifically, someapplications relate to methods and apparatus for monitoring a subject bymonitoring the subject's respiration rate and/or the subject's heartrate.

BACKGROUND

Chronic diseases are often expressed by episodic worsening of clinicalsymptoms. Preventive treatment of chronic diseases reduces the overalldosage of required medication and associated side effects, and lowersmortality and morbidity. Generally, preventive treatment should beinitiated or intensified as soon as the earliest clinical symptoms aredetected, in order to prevent progression and worsening of the clinicalepisode and to stop and reverse the pathophysiological process.Therefore, the ability to accurately monitor pre-episodic indicatorsincreases the effectiveness of preventive treatment of chronic diseases.

Many chronic diseases cause systemic changes in vital signs, such asbreathing and heartbeat patterns, through a variety of physiologicalmechanisms. For example, common respiratory disorders, such as asthma,chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF),are direct modifiers of breathing and/or heartbeat patterns. Otherchronic diseases, such as diabetes, epilepsy, and certain heartconditions (e.g., congestive heart failure (CHF)), are also known tomodify cardiac and breathing activity. In the case of certain heartconditions, such modifications typically occur because ofpathophysiologies related to fluid retention and general cardiovascularinsufficiency. Other signs such as coughing and sleep restlessness arealso known to be of importance in some clinical situations.

Many chronic diseases induce systemic effects on vital signs. Forexample, some chronic diseases interfere with normal breathing andcardiac processes during wakefulness and sleep, causing abnormalbreathing and heartbeat patterns.

Breathing and heartbeat patterns may be modified via various direct andindirect physiological mechanisms, resulting in abnormal patternsrelated to the cause of modification. Some respiratory diseases, such asasthma, and some heart conditions, such as CHF, are direct breathingmodifiers. Other metabolic abnormalities, such as hypoglycemia and otherneurological pathologies affecting autonomic nervous system activity,are indirect breathing modifiers.

Asthma is a chronic disease with no known cure. Substantial alleviationof asthma symptoms is possible via preventive therapy, such as the useof bronchodilators and anti-inflammatory agents. Asthma management isaimed at improving the quality of life of asthma patients.

Monitoring of lung function is viewed as a major factor in determiningan appropriate treatment, as well as in patient follow-up. Preferredtherapies are often based on aerosol-type medications to minimizesystemic side-effects. The efficacy of aerosol type therapy is highlydependent on patient compliance, which is difficult to assess andmaintain, further contributing to the importance of lung-functionmonitoring.

Asthma episodes usually develop over a period of several days, althoughthey may sometimes seem to appear unexpectedly. The gradual onset of theasthmatic episode provides an opportunity to start countermeasures tostop and reverse the inflammatory process. Early treatment at thepre-episode stage may reduce the clinical episode manifestationconsiderably, and may even prevent the transition from the pre-clinicalstage to a clinical episode altogether.

Two techniques are generally used for asthma monitoring. The firsttechnique, spirometry, evaluates lung function using a spirometer, aninstrument that measures the volume of air inhaled and exhaled by thelungs. Airflow dynamics are measured during a forceful, coordinatedinhalation and exhalation effort by the patient into a mouthpiececonnected via a tube to the spirometer. A peak-flow meter is a simplerdevice that is similar to the spirometer, and is used in a similarmanner. The second technique evaluates lung function by measuringnitric-oxide concentration using a dedicated nitric-oxide monitor. Thepatient breathes into a mouthpiece connected via a tube to the monitor.

Efficient asthma management requires daily monitoring of respiratoryfunction, which is generally impractical, particularly in non-clinicalor home environments. Peak-flow meters and nitric-oxide monitors providea general indication of the status of lung function. However, thesemonitoring devices do not possess predictive value, and are used asduring-episode markers. In addition, peak-flow meters and nitric-oxidemonitors require active participation of the patient, which is difficultto obtain from many children and substantially impossible to obtain frominfants.

CHF is a condition in which the heart is weakened and unable tocirculate blood to meet the body's needs. The subsequent buildup offluids in the legs, kidneys, and lungs characterizes the condition ascongestive. The weakening may be associated with either the left, right,or both sides of the heart, with different etiologies and treatmentsassociated with each type. In most cases, it is the left side of theheart which fails, so that it is unable to efficiently pump blood to thesystemic circulation. The ensuing fluid congestion of the lungs resultsin changes in respiration, including alterations in rate and/or pattern,accompanied by increased difficulty in breathing and tachypnea.

Quantification of such abnormal breathing provides a basis for assessingCHF progression. For example, Cheyne-Stokes Respiration (CSR) is abreathing pattern characterized by rhythmic oscillation of tidal volumewith regularly recurring periods of alternating apnea and hyperpnea.While CSR may be observed in a number of different pathologies (e.g.,encephalitis, cerebral circulatory disturbances, and lesions of thebulbar center of respiration), it has also been recognized as anindependent risk factor for worsening heart failure and reduced survivalin patients with CHF. In CHF, CSR is associated with frequent awakeningthat fragments sleep, and with concomitant sympathetic activation, bothof which may worsen CHF. Other abnormal breathing patterns may involveperiodic breathing, prolonged expiration or inspiration, or gradualchanges in respiration rate usually leading to tachypnea.

SUMMARY OF THE INVENTION

For some applications of the present invention, a subject's respirationrate is monitored for a duration of time of greater than two hours. Aparameter of the subject's respiration rate over the time duration, suchas the median respiration rate, the mean respiration rate, the maximumrespiration rate, and/or a pattern of the respiration rate isdetermined. The parameter is compared to the same parameter asdetermined on a previous day during a time period that overlaps with(e.g., is substantially the same as, or partially overlaps with) thetime period based upon which the parameter of respiration was determinedon the present day. For example, the parameter is compared to the sameparameter as determined on a previous day for the same time duration andat the same period (e.g., the same time) of the day. For example, themean respiration rate over a time duration of three hours, between thetimes of 8 pm and 11 pm on the present day, may be compared with themean respiration rate over a time duration of three hours between thetimes of 8 pm and 11 pm on the previous day. In response thereto, thelikelihood of the subject subsequently undergoing an adverse clinicalevent is determined. Typically, it is determined that the subject islikely to undergo an adverse clinical event by determining that thedifference between the parameter of respiration (e.g., the meanrespiration rate) of the present day and of the previous day is greaterthan a threshold amount, e.g., by determining that the parameter ofrespiration of the present day and that of the previous day aresubstantially different. Typically, in response to determining that thesubject is likely to undergo an adverse clinical event, an alert isgenerated.

For some applications, the techniques described in the above paragraphwith respect to the subject's respiration rate are applied with respectto the subject's heart rate and/or with respect to the subject'srespiration rate and the subject's heart rate. For example, it may bedetermined that the subject is likely to undergo an adverse clinicalevent by determining that the difference between a parameter of thesubject's cardiac cycle (e.g., the mean heart rate over a time durationof greater than two hours at a given period of the day) of the presentday and of a previous day is greater than a threshold amount, e.g., bydetermining that the parameter of the cardiac cycle of the present dayand that of the previous day are substantially different. Or, it may bedetermined that the subject is likely to undergo an adverse clinicalevent by determining that the difference between a parameter of thesubject's cardiac cycle of the present day and of a previous day isgreater than a threshold amount, and the difference between a parameterof the subject's respiration of the present day and of a previous day isgreater than a threshold amount.

For some applications of the present invention, a subject's motion ismonitored for a duration of time of greater than two hours. A parameterof the subject's motion, such as total duration that the subject is inmotion, or percentage of time that the subject is in motion, over thetime duration is determined. The parameter is compared to the sameparameter as determined on a previous day during a time period thatoverlaps with (e.g., is substantially the same as, or partially overlapswith) the time period based upon which the parameter of respiration wasdetermined on the present day. For example, the parameter is compared tothe same parameter as determined on a previous day for the same timeduration and at the same period (e.g., the same time) of the day. Forexample, the total time that the subject is in motion, or percentage oftime that the subject is in motion over a time duration of three hours,between the times of 8 pm and 11 pm on the present day, may be comparedwith the total time that the subject is in motion, or percentage of timethat the subject is in motion over a time duration of three hoursbetween the times of 8 pm and 11 pm on the previous day. In responsethereto, the likelihood of the subject subsequently undergoing anadverse clinical event is determined. Typically, it is determined thatthe subject is likely to undergo an adverse clinical event bydetermining that the difference between the parameter of motion of thepresent day and of the previous day is greater than a threshold amount,e.g., by determining that the parameter of motion of the present day andthat of the previous day are substantially different. Typically, inresponse to determining that the subject is likely to undergo an adverseclinical event, an alert is generated.

For some applications, the threshold of the cardiac cycle (describedhereinabove) is set responsively to a detected respiration rate, and/orresponsively to a detected parameter of the subject's motion.Alternatively or additionally, the threshold of the parameter of thesubject's respiration (described hereinabove) is set responsively to thedetected heart rate, and/or responsively to a detected parameter of thesubject's motion. Further alternatively or additionally, the thresholdof the parameter of the subject's motion (described hereinabove) is setresponsively to the detected heart rate, and/or responsively to thedetected respiration rate.

In some embodiments, the present invention includes systems and methodsfor monitoring uterine contractions, for example, for predicting theonset of preterm labor. Such systems may include a motion acquisitionmodule, a pattern analysis module, and an output module. Aspects of thisinvention may be used for monitoring uterine contractions and predictingthe onset of preterm labor, for example, without viewing or touching thepregnant woman's body, for instance, without obtaining compliance fromthe woman.

Another embodiment of the invention is a method for detecting uterinecontractions in a pregnant woman, the method comprising sensing motionof the woman, for example, without contacting the woman, and generatinga signal corresponding to the sensed motion; and analyzing the signal todetect presence of labor contractions. In one aspect, sensing motion ofthe women comprises sensing motion in the lower abdomen, the pelvis, andthe upper abdomen of the women and generating a motion-related signalfor the lower abdomen, the pelvis, and the upper abdomen to detect thepresence of labor contractions.

Another embodiment of the invention is an apparatus for detectinguterine contractions in a pregnant woman, the apparatus comprising atleast one motion sensor adapted to detect motion of the woman, forexample, without contacting the woman, and generate at least one signalcorresponding to the sensed motion; and a signal analyzer adapted toanalyze the at least one signal to detect the presence of laborcontractions.

Other embodiments of the invention include methods and systems formonitoring a clinical condition including monitoring clinical parametersduring sleep and identifying sleep stages and comparing the clinicalparameters in at least one sleep stage to baseline clinical parametersfor that sleep stage. The methods and device for identifying sleepstages may include a motion acquisition module, a pattern analysismodule and an output module, as described below.

There is therefore provided, in accordance with some applications of thepresent invention, apparatus, including:

a mechanical sensor configured to detect a physiological signal of asubject without contacting or viewing the subject or clothes that thesubject is wearing;

a control unit configured to:

-   -   receive the physiological signal from the sensor over a time        duration of at least two hours at a given period of at least one        first baseline day,    -   determine a physiological parameter of the subject based upon        the received physiological signal of the first baseline day;    -   receive the physiological signal from the sensor over a time        duration of at least two hours at a given period of a second        day, the period over which the subject's physiological signal is        detected on the second day overlapping with the period over        which the subject's physiological signal is detected on the        first baseline day;    -   determine a physiological parameter of the subject based upon        the received physiological signal of the second day;    -   compare the physiological parameter based upon the received        physiological signal of the second day to the baseline        physiological parameter of the subject; and    -   generate an alert in response to the comparison; and an output        unit configured to output the alert.

For some applications, the physiological sensor is configured to detectthe physiological signal of the subject by detecting a respiration rateof the subject.

For some applications, the physiological sensor is configured to detectthe physiological signal of the subject by detecting a heart rate of thesubject.

For some applications, the physiological sensor is configured to detectthe physiological signal of the subject by detecting a parameter ofmotion of the subject.

There is further provided, in accordance with some applications of thepresent invention, apparatus, including:

a sensor configured to detect a respiration signal indicative of arespiration rate of a subject; and

a control unit configured to:

-   -   receive the detected respiration signal from the sensor over a        time duration of at least two hours at a given period of at        least one first respiration-rate baseline day;    -   determine a baseline parameter of the subject's respiration        based upon the received respiration signal of the first        respiration-rate baseline day;    -   receive the detected respiration signal from the sensor over a        time duration of at least two hours at a given period of a        second day, the period over which the subject's respiration is        detected on the second day overlapping with the period over        which the subject's respiration is detected on the first        respiration-rate baseline day;    -   determine a parameter of the subject's respiration based upon        the received respiration signal of the second day;    -   compare the parameter of the subject's respiration based upon        the received respiration signal of the second day to the        baseline parameter of the subject's respiration; and    -   generate an alert in response to the comparison; and an output        unit configured to output the alert.

For some applications, the control unit is configured to determine thebaseline parameter of respiration by determining a baseline respirationpattern based upon the received respiration signal of the firstrespiration-rate baseline day, and the control unit is configured todetermine the parameter of the subject's respiration based upon thereceived respiration signal of the second day by determining arespiration pattern based upon the received respiration signal of thesecond day.

For some applications:

the control unit is configured to determine the baseline parameter ofrespiration by determining a parameter selected from the groupconsisting of: a mean respiration rate, a maximum respiration rate, anda median respiration rate, based upon the received respiration signal ofthe first respiration-rate baseline day, and

the control unit is configured to determine the parameter of thesubject's respiration based upon the received respiration signal of thesecond day by determining a parameter selected from the group consistingof: a mean respiration rate, a maximum respiration rate, and a medianrespiration rate, based upon the received respiration signal of thesecond day.

For some applications, the control unit is configured to:

receive a heart-rate signal from the sensor indicative of a heart rateof the subject over a time duration of at least two hours at a givenperiod of at least one first heart-rate baseline day;

determine a baseline parameter of the subject's cardiac cycle based uponthe received heart-rate signal of the first heart-rate baseline day;

receive a heart-rate signal from the sensor indicative of a heart rateof the subject over a time duration of at least two hours at the givenperiod of the second day, the period over which the subject's heart rateis detected on the second day overlapping with the period over which thesubject's heart rate is detected on the first heart-rate baseline day;

determine a parameter of the subject's cardiac cycle based upon thereceived heart-rate signal of the second day; and

compare the parameter of the subject's cardiac cycle based upon thereceived heart-rate signal of the second day to the baseline parameterof the cardiac cycle, and

generate the alert by generating the alert in response to (a) thecomparison of the parameter of the subject's respiration based upon thereceived respiration signal of the second day to the baseline parameterof the subject's respiration, and (b) the comparison of the parameter ofthe subject's cardiac cycle based upon the received heart-rate signal ofthe second day to the baseline parameter of the subject's cardiac cycle.

For some applications, the control unit is configured to:

receive a motion signal from the sensor indicative of motion of thesubject over a time duration of at least two hours at a given period ofat least one first motion-parameter baseline day;

determine a baseline parameter of the subject's motion based upon thereceived motion signal of the first motion-parameter baseline day;

receive a motion signal from the sensor indicative of motion of thesubject over a time duration of at least two hours at the given periodof the second day, the period over which the subject's motion isdetected on the second day overlapping with the period over which thesubject's motion is detected on the first motion-parameter baseline day;

determine a parameter of the subject's motion based upon the receivedmotion signal of the second day; and

compare the parameter of the subject's motion based upon the receivedmotion signal of the second day to the baseline parameter of motion, and

generate the alert by generating the alert in response to (a) thecomparison of the parameter of the subject's respiration based upon thereceived respiration signal of the second day to the baseline parameterof the subject's respiration, and (b) the comparison of the parameter ofthe subject's motion based upon the received motion signal of the secondday to the baseline parameter of the subject's motion.

For some applications, the control unit is configured to compare theparameter of the subject's respiration based upon the receivedrespiration signal of the second day to the baseline parameter of thesubject's respiration by determining whether the parameter of thesubject's respiration based upon the received respiration signal of thesecond day differs from the baseline parameter of the subject'srespiration by more than a threshold amount.

For some applications, the control unit is configured to:

receive a heart-rate signal from the sensor indicative of a heart rateof the subject; and

set the threshold in response to the detected heart-rate signal.

For some applications, the control unit is configured to:

receive a motion signal from the sensor indicative of a motion of thesubject; and

set the threshold in response to the detected motion signal.

There is additionally provided, in accordance with some applications ofthe present invention, apparatus, including:

a sensor configured to detect a heart-rate signal indicative of a heartrate of a subject; and

a control unit configured to:

-   -   receive the detected heart-rate signal from the sensor over a        time duration of at least two hours at a given period of at        least one first heart-rate baseline day;    -   determine a baseline parameter of the subject's cardiac cycle        based upon the received heart-rate signal of the first        heart-rate baseline day;    -   receive the detected heart-rate signal from the sensor over a        time duration of at least two hours at a given period of a        second day, the period over which the subject's heart rate is        detected on the second day overlapping with the period over        which the subject's heart rate is detected on the first        heart-rate baseline day;    -   determine a parameter of the subject's cardiac cycle based upon        the received heart-rate signal of the second day;    -   compare the parameter of the subject's cardiac cycle based upon        the received heart-rate signal of the second day to the baseline        parameter of the subject's cardiac cycle; and    -   generate an alert in response to the comparison; and

an output unit configured to output the alert.

For some applications, the control unit is configured to:

receive a motion signal from the sensor indicative of motion of thesubject over a time duration of at least two hours at a given period ofat least one first motion-parameter baseline day;

determine a baseline parameter of the subject's motion based upon thereceived motion signal of the first motion-parameter baseline day;

receive a motion signal from the sensor indicative of motion of thesubject over a time duration of at least two hours at the given periodof the second day, the period over which the subject's motion isdetected on the second day overlapping with the period over which thesubject's motion is detected on the first motion-parameter baseline day;

determine a parameter of the subject's motion based upon the receivedmotion signal of the second day; and

compare the parameter of the subject's motion based upon the receivedmotion signal of the second day to the baseline parameter of motion, and

generate the alert by generating the alert in response to (a) thecomparison of the parameter of the subject's cardiac cycle based uponthe received heart-rate signal of the second day to the baselineparameter of the subject's cardiac cycle, and (b) the comparison of theparameter of the subject's motion based upon the received motion signalof the second day to the baseline parameter of the subject's motion.

For some applications, the control unit is configured to compare theparameter of the subject's cardiac cycle based upon the receivedheart-rate signal of the second day to the baseline parameter of thesubject's cardiac cycle by determining whether the parameter of thesubject's cardiac cycle based upon the received heart-rate signal of thesecond day differs from the baseline parameter of the subject's cardiaccycle by more than a threshold amount.

For some applications, the control unit is configured to:

receive a respiration signal from the sensor indicative of a respirationrate of the subject; and

set the threshold in response to the detected respiration signal.

For some applications, the control unit is configured to:

receive a motion signal from the sensor indicative of a motion of thesubject; and

set the threshold in response to the detected motion signal.

There is further provided, in accordance with some applications of thepresent invention, apparatus, including:

a sensor configured to detect a motion signal indicative of motion of asubject; and

a control unit configured to:

-   -   receive the detected motion signal from the sensor over a time        duration of at least two hours at a given period of at least one        first motion-parameter baseline day;    -   determine a baseline parameter of the subject's motion based        upon the received motion signal of the first motion-parameter        baseline day;    -   receive the detected motion signal from the sensor over a time        duration of at least two hours at a given period of a second        day, the period over which the subject's motion is detected on        the second day overlapping with the period over which the        subject's motion is detected on the first motion-parameter        baseline day;    -   determine a parameter of the subject's motion based upon the        received motion signal of the second day;    -   compare the parameter of the subject's motion based upon the        received motion signal of the second day to the baseline        parameter of the subject's motion; and    -   generate an alert in response to the comparison; and an output        unit configured to output the alert.

For some applications, the control unit is configured to compare theparameter of the subject's motion based upon the received motion signalof the second day to the baseline parameter of the subject's motion bydetermining whether the parameter of the subject's motion based upon thereceived motion signal of the second day differs from the baselineparameter of the subject's motion by more than a threshold amount.

For some applications, the control unit is configured to:

receive a respiration signal from the sensor indicative of a respirationrate of the subject; and

set the threshold in response to the detected respiration signal.

For some applications, the control unit is configured to:

receive a heart-rate signal from the sensor indicative of a heart rateof the subject; and

set the threshold in response to the detected heart-rate signal.

There is additionally provided, in accordance with some applications ofthe present invention, a method including:

detecting a respiration rate of a subject over a time duration of atleast two hours at a given period of at least one first respiration-ratebaseline day;

determining a baseline parameter of the subject's respiration based uponthe detected respiration rate for the first respiration-rate baselineday;

detecting a respiration rate of the subject over a time duration of atleast two hours at a given period of a second day, the period over whichthe subject's respiration is detected on the second day overlapping withthe period over which the subject's respiration is detected on the firstrespiration-rate baseline day;

determining a parameter of the subject's respiration based upon thedetected respiration rate on the second day;

comparing the parameter of the subject's respiration based upon thedetected respiration rate on the second day to the baseline parameter ofthe subject's respiration; and

generating an alert in response to the comparison.

There is further provided, in accordance with some applications of thepresent invention, a method including:

detecting a heart rate of a subject over a time duration of at least twohours at a given period of at least one first heart-rate baseline day;

determining a baseline parameter of the subject's cardiac cycle basedupon the detected heart rate for the first heart-rate baseline day;

detecting a heart rate of the subject over a time duration of at leasttwo hours at a given period of a second day, the period over which thesubject's heart rate is detected on the second day overlapping with theperiod over which the subject's heart rate is detected on the firstheart-rate baseline day;

determining a parameter of the subject's cardiac cycle based upon thedetected heart rate on the second day;

comparing the parameter of the subject's cardiac cycle based upon thedetected heart rate on the second day to the baseline parameter of thesubject's cardiac cycle; and

generating an alert in response to the comparison.

There is additionally provided, in accordance with some applications ofthe present invention, a method including:

detecting motion of a subject over a time duration of at least two hoursat a given period of at least one first motion-parameter baseline day;

determining a motion parameter of the subject's respiration based uponthe detected motion for the first motion-parameter baseline day;

detecting motion of the subject over a time duration of at least twohours at a given period of a second day, the period over which thesubject's motion is detected on the second day overlapping with theperiod over which the subject's motion is detected on the firstmotion-parameter baseline day;

determining a parameter of the subject's motion based upon the motiondetected on the second day;

comparing the parameter of the subject's motion based upon the motiondetected on the second day to the baseline parameter of the subject'smotion; and

generating an alert in response to the comparison.

There is further provided, in accordance with some applications of thepresent invention, a method including:

detecting a physiological signal of a subject over a time duration of atleast two hours at a given period of at least one first baseline day,without contacting or viewing the subject or clothes that the subject iswearing;

determining a physiological parameter of the subject based upon thedetected physiological signal for the first baseline day;

detecting the physiological signal of the subject over a time durationof at least two hours at a given period of a second day, the period overwhich the subject's physiological signal is detected on the second dayoverlapping with the period over which the physiological signal isdetected on the first baseline day;

determining a physiological parameter of the subject based upon thedetected physiological signal on the second day;

comparing the physiological parameter based upon the detectedphysiological signal on the second day to the baseline physiologicalparameter of the subject; and

generating an alert in response to the comparison.

There is therefore provided, in accordance with some applications of thepresent invention, apparatus for monitoring a clinical condition of asubject, the apparatus including:

a motion sensor configured to monitor the subject, and to generate asignal in response thereto; and

a control unit, configured to:

-   -   analyze the signal,    -   in response to the analyzing, (a) identify a sleep stage of the        subject, and (b) identify a clinical parameter of the subject in        the identified sleep stage,    -   monitor the clinical condition, by comparing the clinical        parameter to a baseline clinical parameter for the identified        sleep stage, and    -   generate an output in response thereto.

In some applications, the sensor is configured to monitor the subjectwithout contacting or viewing the subject or clothes the subject iswearing.

In some applications, the identified sleep stage is a slow-wave sleepstage, the control unit being configured to identify the clinicalparameter of the subject in the slow-wave sleep stage.

In some applications, the identified sleep stage is a rapid-eye-movement(REM) sleep stage, the control unit being configured to identify theclinical parameter of the subject in the REM sleep stage.

In some applications, the clinical parameter is selected from the groupconsisting of: respiratory rate, and heart rate, the control unit beingconfigured to identify the selected clinical parameter.

In some applications,

the clinical parameter is a left ventricular ejection time (LVET) of thesubject,

the control unit being configured to identify the LVET of the subject.

In some applications, the control unit is configured to:

identify an average of a clinical parameter for the identified sleepstage, and

monitor the clinical condition, by comparing the average to thebaseline.

In some applications, the control unit is configured to:

identify the average of the clinical parameter for each hour of sleep,and

monitor the clinical condition, by comparing each of the averages to arespective baseline.

In some applications, the control unit is configured to:

analyze the signal at a plurality of times,

in response to the analyzing, (a) ascertain that the sleep stage of thesubject at each of the plurality of times is a single given sleep stage,and (b) compute a statistic of the clinical parameter over the pluralityof times, and

monitor the clinical condition, by comparing the statistic to thebaseline.

In some applications,

the plurality of times is a second plurality of times,

the statistic is a second statistic,

the baseline is a first statistic of the clinical parameter over a firstplurality of times that precedes the second plurality of times, and

the control unit is further configured to:

-   -   analyze the signal at the first plurality of times, and    -   in response to the analyzing, (a) ascertain that a sleep stage        of the subject at each of the first plurality of times is the        given sleep stage, and (b) compute the first statistic.

In some applications,

the baseline is a value of the clinical parameter exhibited during afirst sleeping session, and

the control unit is configured to identify the clinical parameter byidentifying a value of the clinical parameter exhibited during a secondsleeping session that follows the first sleeping session.

In some applications,

the baseline is a value of the clinical parameter exhibited at a firsttime during a sleeping session, and

the control unit is configured to identify the clinical parameter byidentifying a value of the clinical parameter exhibited at a second timeduring the sleeping session that follows the first time.

In some applications, the control unit is configured to monitor theclinical condition by identifying a likelihood that the subject hasfever.

In some applications,

the control unit is further configured to, in response to analyzing thesignal, identify that breathing of the subject is labored, and

the control unit is configured to identify a likelihood that the subjecthas fever in response to: (a) comparing the identified clinicalparameter to the baseline, and (b) identifying that breathing of thesubject is labored.

In some applications,

the clinical parameter is a left ventricular ejection time (LVET) of thesubject,

the control unit being configured to identify a likelihood that thesubject has fever in response to comparing the identified LVET to abaseline LVET.

There is further provided, in accordance with some applications of thepresent invention, apparatus for monitoring a subject, the apparatusincluding:

a physiological sensor, configured to detect a heart rate of the subjectduring a sleeping session of the subject, and to generate a sensorsignal in response thereto; and

a control unit, configured to:

-   -   analyze the sensor signal,    -   in response to the analyzing, identify a likelihood that the        subject ate within a given amount of time before the sleeping        session, and    -   in response to the identifying, generate an output signal        indicative that the subject ate within the given amount of time.

In some applications, the physiological sensor is configured to detectthe heart rate of the subject without contacting or viewing the subjector clothes the subject is wearing.

In some applications, the control unit is configured to identify thelikelihood by determining that the heart rate of the subject is greaterthan a baseline heart rate.

In some applications, the control unit is configured to identify thelikelihood by determining that the heart rate of the subject does notincrease over a particular interval by more than a threshold.

There is further provided, in accordance with some applications of thepresent invention, apparatus for monitoring a subject, the apparatusincluding:

a motion sensor, configured to detect motion of the subject and togenerate a signal in response thereto; and

a control unit, configured to:

-   -   analyze the signal,    -   in response to the analyzing, identify a clinical parameter of        the subject,    -   in response to an environmental change, identify a baseline        value for the clinical parameter,    -   derive a score based on a deviation of the identified clinical        parameter from the baseline value,    -   in response to the score, perform an action selected from the        group consisting of: determine whether a clinical episode is        predicted, determine whether a clinical episode is currently        occurring, and monitor an occurring clinical episode, and    -   generate an output in response thereto.

In some applications, the control unit is configured to identify thebaseline value in response to a change in seasons.

There is further provided, in accordance with some applications of thepresent invention, apparatus for monitoring a subject, the apparatusincluding:

a motion sensor, configured to detect motion of the subject and togenerate a signal in response thereto; and

a control unit, configured to:

-   -   analyze the signal,    -   in response to the analyzing, identify a clinical parameter of        the subject,    -   identify a baseline value for the clinical parameter that        corresponds to a day of the week,    -   derive a score based on a deviation of the identified clinical        parameter from the baseline value,    -   in response to the score, perform an action selected from the        group consisting of: determine whether a clinical episode is        predicted, determine whether a clinical episode is currently        occurring, and monitor an occurring clinical episode, and    -   generate an output in response thereto.

In some applications, the control unit is configured to identify thebaseline value in response to a weekly schedule of the subject.

In some applications, the control unit is configured to identify thebaseline value in response to physical activity of the subject generallyrecurring on a particular day of the week.

In some applications, the control unit is configured to identify a firstbaseline value corresponding to a weekend day, and a second baselinevalue, which is different from the first baseline value, correspondingto a weekday.

In some applications, the clinical parameter is a respiration rate ofthe subject, and the control unit is configured to identify a higherbaseline value for a weekend day than for a weekday.

There is further provided, in accordance with some applications of thepresent invention, apparatus for monitoring a subject, the apparatusincluding:

a motion sensor, configured to detect motion of the subject and togenerate a signal in response thereto; and

a control unit, configured to:

-   -   analyze the signal,    -   in response to the analyzing, identify a clinical parameter of        the subject,    -   in response to a menstrual cycle of the subject, identify a        baseline value of the clinical parameter,    -   derive a score based on a deviation of the identified clinical        parameter from the baseline value,    -   in response to the score, perform an action selected from the        group consisting of: determine whether a clinical episode is        predicted, determine whether a clinical episode is currently        occurring, and monitor an occurring clinical episode, and    -   generate an output in response thereto.

There is further provided, in accordance with some applications of thepresent invention, apparatus for monitoring a subject, the apparatusincluding:

a temperature sensor, configured to detect a room temperature;

a motion sensor, configured to detect motion of the subject and togenerate a signal in response thereto; and

a control unit, configured to:

-   -   analyze the signal,    -   in response to the analyzing, identify a clinical parameter of        the subject,    -   in response to the room temperature, identify a baseline value        for the clinical parameter,    -   derive a score based on a deviation of the identified clinical        parameter from the baseline value,    -   in response to the score, perform an action selected from the        group consisting of: determine whether a clinical episode is        predicted, determine whether a clinical episode is currently        occurring, and monitor an occurring clinical episode, and    -   generate an output in response thereto.

The present invention will be more fully understood from the followingdetailed description of applications thereof, taken together with thedrawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system for monitoring a chronicmedical condition of a subject, in accordance with some applications ofthe present invention;

FIG. 2 is a schematic block diagram illustrating components of a controlunit of the system of FIG. 1, in accordance with some applications ofthe present invention;

FIGS. 3A-D are graphs showing the results of experiments conducted, inaccordance with some applications of the present invention;

FIG. 4 is a graph illustrating breathing rate patterns of a chronicasthma patient, which is the same as FIG. 4 of U.S. Pat. No. 7,077,810to Lange, which is incorporated herein by reference;

FIGS. 5 and 6 are graphs of exemplary baseline and measured breathingrate and heart rate nighttime patterns, respectively, which aregenerally similar to FIGS. 6 and 7 of U.S. Pat. No. 7,314,451 toHalperin, which is incorporated herein by reference;

FIG. 7 is a graph of baseline and breathing rate nighttime patterns,respectively, which is the same as FIG. 23 of U.S. Pat. No. 7,314,451 toHalperin;

FIG. 8 is a schematic illustration of apparatus for monitoring asubject, in accordance with some applications of the present invention;

FIGS. 9 and 10 show plots of data obtained from a motion sensor, inaccordance with some applications of the present invention; and

FIG. 11 shows plots of data obtained from a sensor, in accordance withsome applications of the present invention.

DETAILED DESCRIPTION OF APPLICATIONS

Reference is made to FIG. 1, which is a schematic illustration of asystem 10 for monitoring a chronic medical condition of a subject 12, inaccordance with some applications of the present invention. System 10typically comprises a mechanical sensor 30 (e.g., a motion sensor), acontrol unit 14, and a user interface 24. For some applications, userinterface 24 is integrated into control unit 14, as shown in the figure,while for other applications, the user interface and control unit areseparate units. For some applications, motion sensor 30 is integratedinto control unit 14, in which case user interface 24 is either alsointegrated into control unit 14 or remote from control unit 14.

FIG. 2 is a schematic block diagram illustrating components of controlunit 14, in accordance with some applications of the present invention.Control unit 14 typically comprises a motion data acquisition module 20and a pattern analysis module 16. Pattern analysis module 16 typicallycomprises one or more of the following modules: a breathing patternanalysis module 22, a heartbeat pattern analysis module 23, a coughanalysis module 26, a restlessness analysis module 28, a blood pressureanalysis module 29, and an arousal analysis module 31. For someapplications, two or more of analysis modules 22, 23, 26, 28, 29, and 31are packaged in a single housing. For other applications, the modulesare packaged separately (for example, so as to enable remote analysis byone or more of the pattern analysis modules of breathing signalsacquired locally by data acquisition module 20). For some applications,user interface 24 comprises a dedicated display unit such as an LCD orCRT monitor. Alternatively or additionally, user interface 24 includes acommunication line for relaying the raw and/or processed data to aremote site for further analysis and/or interpretation.

For some applications of the present invention, data acquisition module20 is adapted to non-invasively monitor breathing and heartbeat patternsof subject 12. Breathing pattern analysis module 22 and heartbeatpattern analysis module 23 are adapted to analyze the respectivepatterns in order to (a) predict an approaching clinical event, such asan asthma attack or heart condition-related lung fluid buildup, and/or(b) monitor the severity and progression of a clinical event as itoccurs. For some applications, breathing pattern analysis module 22 andheartbeat pattern analysis module 23 are adapted to analyze therespective patterns in order to determine a likelihood of an approachingadverse clinical event without necessarily identifying the nature of theevent. User interface 24 (e.g., via a speaker 240) is adapted to notifysubject 12 and/or a healthcare worker of the predicted or occurringevent. Prediction of an approaching clinical event facilitates earlypreventive treatment, which generally reduces the required dosage ofmedication, and/or lowers mortality and morbidity. When treating asthma,such a reduced dosage generally minimizes the side-effects associatedwith high dosages typically required to reverse the inflammatorycondition once the event has begun.

For some applications of the present invention, pattern analysis module16 combines parameter data generated from two or more of analysismodules 22, 23, 26, 28, 29, and analyzes the combined data in order topredict and/or monitor a clinical event. For some applications, patternanalysis module 16 derives a score for each parameter based on theparameter's deviation from baseline values (either for the specificpatient or based on population averages). Pattern analysis module 16combines the scores, such as by taking an average, maximum, standarddeviation, or other function of the scores. The combined score iscompared to one or more threshold values (which may be predetermined) todetermine whether an event is predicted, currently occurring, or neitherpredicted nor occurring, and/or to monitor the severity and progressionof an occurring event. For some applications, pattern analysis module 16learns the criteria and/or functions for combining the individualparameter scores for the specific patient or patient group based onpersonal history. For example, pattern analysis module 16 may performsuch learning by analyzing parameters measured prior to previousclinical events.

Although system 10 may monitor breathing and heartbeat patterns at anytime, for some conditions it is generally most effective to monitor suchpatterns during sleep at night. When the subject is awake, physical andmental activities unrelated to the monitored condition often affectbreathing and heartbeat patterns. Such unrelated activities generallyhave less influence during most night sleep. For some applications,system 10 monitors and records patterns throughout all or a largeportion of a night. The resulting data set generally encompasses typicallong-term respiratory and heartbeat patterns, and facilitatescomprehensive analysis. Additionally, such a large data set enablesrejection of segments contaminated with movement or other artifacts,while retaining sufficient data for a statistically significantanalysis.

Reference is again made to FIG. 2. Data acquisition module 20 typicallycomprises circuitry for processing the raw motion signal generated bymotion sensor 30, such as at least one pre-amplifier 32, at least onefilter 34, and an analog-to-digital (A/D) converter 36. Filter 34typically comprises a band-pass filter or a low-pass filter, serving asan anti-aliasing filter with a cut-off frequency of less than one halfof the sampling rate. The low-passed data is typically digitized at asampling rate of at least 10 Hz and stored in memory. For example, theanti-aliasing filter cut-off may be set to 5 Hz and the sampling rateset to 40 Hz.

Reference is again made to FIG. 1. Typically, motion sensor 30 detectsone or more physiological signal of the subject without contacting orviewing the subject or clothes that the subject is wearing. For someapplications of the present invention, motion sensor 30 comprises apressure gauge (e.g., a piezoelectric sensor) or a strain gauge (e.g., asilicon or other semiconductor strain gauge, or a metallic straingauge), which is typically adapted to be installed in, on, or under areclining surface 37 upon which the subject lies, e.g., sleeps, and tosense breathing- and heartbeat-related motion of the subject. “Pressuregauge,” as used in the claims, includes, but is not limited to, all ofthe gauges mentioned in the previous sentence. Typically, recliningsurface 37 comprises a mattress, a mattress covering, a sheet, amattress pad, and/or a mattress cover. For some applications, motionsensor 30 is integrated into reclining surface 37, e.g., into amattress, and the motion sensor and reclining surface are providedtogether as an integrated unit. For some applications, motion sensor 30is adapted to be installed in, on, or under reclining surface 37 in avicinity of an abdomen 38 or chest 39 of subject 12. Alternatively oradditionally, motion sensor 30 is installed in, on, or under recliningsurface 37 in a vicinity of a portion of subject 12 anatomically below awaist of the subject, such as in a vicinity of legs 40 of the subject.For some applications, such positioning provides a clearer pulse signalthan positioning the sensor in a vicinity of abdomen 38 or chest 39 ofthe subject. For some applications, motion sensor 30 comprises a fiberoptic sensor, for example, as described by Butter and Hocker in AppliedOptics 17: 2867-2869 (Sep. 15, 1978).

For some applications, the pressure or strain gauge is encapsulated in arigid compartment, which typically has a surface area of at least 10cm̂2, and a thickness of less than mm. The gauge output is channeled toan electronic amplifier, such as a charge amplifier typically used withpiezoelectric accelerometers and capacitive transducers to condition theextremely high output impedance of the transducer to a low impedancevoltage suitable for transmission over long cables. The strain gauge andelectronic amplifier translate the mechanical vibrations into electricalsignals. Alternatively, the strain gauge output is amplified using aWheatstone bridge and an amplifier such as Analog Device Module Numbers3B16, for a minimal bandwidth, or 3B18, for a wider bandwidth (NationalInstruments Corporation, Austin, Tex., USA).

For some applications of the present invention, motion sensor 30comprises a grid of multiple pressure or strain gauge sensors, adaptedto be installed in, on, or under reclining surface 37. The use of such agrid, rather than a single gauge, may improve breathing and heartbeatsignal reception.

Breathing pattern analysis module 22 is adapted to extract breathingpatterns from the motion data, and heartbeat pattern analysis module 23is adapted to extract heartbeat patterns from the motion data.Alternatively or additionally, system 10 comprises another type ofsensor, such as an acoustic or air-flow sensor, attached or directed atthe subject's face, neck, chest and/or back.

For some applications of the present invention, the subject'srespiration rate is monitored for a duration of time of greater than twohours (e.g., greater than three hours, greater than four hours, greaterthan five hours, or greater than six hours). Breathing pattern analysismodule 22 determines a parameter of the subject's respiration rate overthe time duration, such as the median respiration rate, the meanrespiration rate, the maximum respiration rate, and/or a respirationrate pattern. Module 22 compares the determined parameter to the sameparameter as determined on a previous day during a time period thatoverlaps with the time period based upon which the parameter ofrespiration was determined on the present day. For example, theparameter is compared to the same parameter as determined on a previousday for the same time duration and at the same period (e.g., the sametime) of the day.

For example, the mean respiration rate over a time duration of threehours, between the times of 8 pm and 11 pm on the present day, may becompared with the mean respiration rate over a time duration of threehours between the times of 8 pm and 11 pm on the previous day. Inresponse thereto, the likelihood of the subject subsequently undergoingan adverse clinical event is determined. Typically, it is determinedthat the subject is likely to undergo an adverse clinical event bydetermining that the difference between the parameter of respiration(e.g., the mean respiration rate) of the present day and of the previousday is greater than a threshold amount. Typically, in response todetermining that the subject is likely to undergo an adverse clinicalevent, an alert is generated by user interface 24.

For some applications, the period of to the day which is compared to thesame period of the previous day is a time period, e.g., between 8 pm and11 pm, as described hereinabove. Alternatively, the period may bedefined with respect to the subject's circadian clock, e.g., the periodmay be the first three hours of the subject's sleep, or from thebeginning of the second hour of the subject's sleep to the end of thefifth hour of the subject's sleep.

For some applications, heartbeat pattern analysis module 23 appliesgenerally similar analysis to the subject's heart rate to that describedhereinabove with respect to the breathing pattern analysis module 22.For example, module 23 may determine that the subject is likely toundergo an adverse clinical event by determining that the differencebetween a parameter of the subject's cardiac cycle (e.g., the mean heartrate over a time duration of greater than two hours at a given period ofthe day) on the present day and that of a previous day is greater than athreshold amount. For some applications, control unit 14 determines thatthe subject is likely to undergo an adverse clinical event bydetermining that the difference between a parameter of the subject'scardiac cycle on the present day and that of a previous day is greaterthan a threshold amount, and the difference between a parameter of thesubject's respiration on the present day and that of the previous day isgreater than a threshold amount.

As described hereinabove, for some applications, breathing patternanalysis module 22 and heartbeat pattern analysis module 23 are adaptedto analyze the respective patterns in order to determine a likelihood ofan approaching adverse clinical event without necessarily identifyingthe nature of the event. Thus, for some applications, in response todetermining that the subject is likely to undergo an adverse clinicalevent, the user interface generates a generic alert signal, in order toindicate to a healthcare professional that an adverse clinical event isimminent.

For some applications, system 10 applies generally similar analysis to adifferent physiological parameter of the subject to that describedhereinabove with respect to the breathing pattern analysis module 22.For example, the system may apply the analysis to a parameter of thesubject's motion, such as the total time that the subject is in motion,or percentage of time that the subject is in motion over a given timeduration.

Reference is now made to FIGS. 3A-D, which are graphs showing theresults of experiments conducted, in accordance with some applicationsof the present invention. Earlysense Ltd. (Israel) manufactures theEverOn™ system, which is a contact-less piezoelectric sensor placedunder a subject's mattress that provides continuous measurement of heartrate and respiration rate of the subject, generally in accordance withthe techniques described hereinabove.

A non-interventional study was conducted in two internal medicinedepartments (Sheba Medical Center and Meir Medical Center, both inIsrael). Patients who were admitted due to an acute respiratorycondition were enrolled on the study. Patients were monitored by theEverOn™ sensor and followed for major clinical episodes. A majorclinical event was defined as death, transfer to ICU, or intubation andmechanical ventilation on the floors. Out of 149 patients included inthe study, 96 patients had a length of stay that allowed at least onecomparable time window. Ten major clinical events were recorded forthese patients. Retrospective analysis of continuous respiratory andheart signal recording was performed. The median respiration rate andheart rate over 6-hour time durations (00-06, 06-12, 12-18, and 18-24)were compared to the median respiration rate and heart rate over acorresponding 6-hour time duration on the previous day. Similarly, themaximum respiration rate and heart rate over 6-hour time durations(00-06, 06-12, 12-18, and 18-24) were compared to the maximumrespiration rate and heart rate over a corresponding 6-hour timeduration on the previous day. Retrospective receiver operatingcharacteristic (ROC) curve analysis was applied to the results todetermine the sensitivity, specificity, positive predictive value, andnegative predictive value of using respective thresholds (i.e.,thresholds for the difference between median or maximum respiration rateor heart rate and those of the previous day) for determining thelikelihood of a subject undergoing (a) any adverse clinical event, i.e.,either a major or a moderate clinical event (such as a non-majorrespiratory event requiring immediate intervention, e.g., bilevelpositive airway pressure (BIPAP) or continuous positive airway pressure(CPAP)), or (b) a major clinical event.

Table 1 (shown below) shows the results of the ROC curve analysis ofrespective combinations of median heart rate and respiration ratethresholds (i.e., thresholds for the difference between median heartrate and respiration rate and those of the previous day) with respect todetermining the likelihood of a subject undergoing any adverse clinicalevent, i.e., either a major or a moderate clinical event.

TABLE 1 Threshold Heart rate (beats per minute) - Respiration rate(breaths per minute) ) Sensitivity Specificity PPV NPV 14-3 67 82 35 9514-4 67 82 35 95 14-5 67 86 40 95 14-6 58 89 44 94 16-3 67 87 42 95 16-467 87 42 95 16-5 67 89 47 95 16-6 58 93 54 94 18-3 67 89 47 95 18-4 6789 47 95 18-5 67 90 50 95 18-6 58 94 58 94 20-3 67 94 62 95 20-4 67 9462 95 20-5 67 95 67 95 20-6 58 98 78 94 22-3 67 94 62 95 22-4 67 94 6295 22-5 67 95 67 95 22-6 58 98 78 94

Table 2 (shown below) shows the results of the ROC curve analysis ofrespective combinations of median heart rate and respiration rate (i.e.,thresholds for the difference between median heart rate and respirationrate and those of the previous day) thresholds with respect todetermining the likelihood of a subject undergoing a major clinicalevent.

TABLE 2 Threshold (Heart rate (beats per minute) - Respiration rate(breaths per minute) ) Sensitivity Specificity PPV NPV 14-3 80 83 35 9714-4 80 83 35 97 14-5 80 86 40 97 14-6 70 90 44 96 16-3 80 87 42 97 16-480 87 42 97 16-5 80 90 47 97 16-6 70 93 54 96 18-3 80 90 47 97 18-4 8090 47 97 18-5 80 91 50 98 18-6 70 94 58 96 20-3 80 94 62 98 20-4 80 9462 98 20-5 80 95 67 98 20-6 70 98 78 97 22-3 80 94 62 98 22-4 80 94 6298 22-5 80 95 67 98 22-6 70 98 78 97

It is noted with respect to Tables 1 and 2 that the greatest sum ofsensitivity and specificity is for thresholds of 20 or 22 for medianheart rate in combination with a threshold of 5 for median respirationrate, both for predicting all adverse clinical events (i.e., major andminor adverse clinical events), and for predicting major clinicalevents.

Thus, for some applications of the present invention, a subject's heartrate and respiration rate are monitored. The median (or mean, ormaximum) heart rate and respiration rate over a time duration of morethan two hours and less than eight hours (e.g., greater than threehours, greater than four hours, greater than five hours, or greater thansix hours) is determined and is compared to the median (or mean, ormaximum) heart rate and respiration rate over a similar time duration ata similar period of the day (e.g., at the same time of day) on at leastone previous day (e.g., the previous day). In response to determining(a) that the median (or mean, or maximum) heart rate on the present daydiffers from that of the previous day by a threshold amount of more than15 beats per minute, e.g., more than 18 beats per minute, and (b) thatthe median (or mean, or maximum) respiration rate of the present daydiffers from that of the previous day by a threshold amount of more than3 breaths per minute, e.g., more than 4 breaths per minute, then analert is generated in order to indicate that an adverse clinical eventis likely to occur.

Table 3 (shown below) shows the results of the ROC curve analysis ofrespective maximum heart rate thresholds (i.e., thresholds for thedifference between the maximum heart rate and that of the previous day)with respect to determining the likelihood of a subject undergoing amajor or a moderate clinical event.

TABLE 3 Heart rate threshold Sum of (beats per Sensitivity and minute)Sensitivity Specificity Specificity 0.00 1.00 0.00 1.00 0.25 1.00 0.011.01 1.00 1.00 0.02 1.02 3.00 0.92 0.07 0.99 4.00 0.83 0.11 0.94 4.500.83 0.17 1.00 5.00 0.83 0.19 1.02 6.00 0.75 0.25 1.00 7.00 0.75 0.321.07 8.00 0.75 0.38 1.13 8.50 0.67 0.46 1.13 9.00 0.67 0.48 1.14 10.000.67 0.54 1.20 11.00 0.67 0.62 1.29 11.50 0.67 0.70 1.37 12.00 0.67 0.711.38 13.00 0.67 0.75 1.42 13.50 0.67 0.79 1.45 14.00 0.67 0.80 1.4615.00 0.67 0.82 1.49 16.00 0.67 0.85 1.51 17.00 0.67 0.86 1.52 18.000.67 0.87 1.54 19.00 0.67 0.89 1.56 20.00 0.67 0.90 1.57 21.00 0.67 0.921.58 22.00 0.67 0.93 1.60 22.75 0.58 0.93 1.51 25.00 0.58 0.94 1.5227.00 0.50 0.95 1.45 28.00 0.42 0.95 1.37 29.00 0.33 0.95 1.29 30.750.17 0.95 1.12 32.00 0.08 0.95 1.04 33.00 0.08 0.96 1.05 34.00 0.08 0.981.06 53.00 0.00 0.98 0.98 56.00 0.00 0.99 0.99

It is noted with respect to Table 3 that the greatest sum of sensitivityand specificity is for a heart rate threshold of 22 beats per minute,for predicting major and moderate adverse clinical events. FIG. 3A showsthe ROC curve for a heart rate threshold of 22 with respect topredicting a likelihood of either a major or a moderate adverse clinicalevent. The area under the curve is 0.70 with a standard deviation of0.11 and a p-value of 0.026.

Table 4 (shown below) shows the results of the ROC curve analysis ofrespective maximum heart rate thresholds (i.e., thresholds for thedifference between the maximum heart rate and that of the previous day)with respect to determining the likelihood of a subject undergoing amajor clinical event.

TABLE 4 Heart rate threshold Sum of (beats per Sensitivity and minute)Sensitivity Specificity Specificity 0.00 1.00 0.00 1.00 0.25 1.00 0.011.01 1.00 1.00 0.02 1.02 3.00 1.00 0.08 1.08 4.00 0.90 0.12 1.02 4.500.90 0.17 1.07 5.00 0.90 0.20 1.10 6.00 0.80 0.26 1.06 7.00 0.80 0.331.13 8.00 0.80 0.38 1.18 8.50 0.80 0.48 1.28 9.00 0.80 0.49 1.29 10.000.80 0.55 1.35 11.00 0.80 0.63 1.43 11.50 0.80 0.71 1.51 12.00 0.80 0.721.52 13.00 0.80 0.76 1.56 13.50 0.80 0.79 1.59 14.00 0.80 0.80 1.6015.00 0.80 0.83 1.63 16.00 0.80 0.85 1.65 17.00 0.80 0.86 1.66 18.000.80 0.87 1.67 19.00 0.80 0.90 1.70 20.00 0.80 0.91 1.71 21.00 0.80 0.921.72 22.00 0.80 0.93 1.73 22.75 0.70 0.93 1.63 25.00 0.70 0.94 1.6427.00 0.60 0.95 1.55 28.00 0.50 0.95 1.45 29.00 0.40 0.95 1.35 30.750.20 0.95 1.15 32.00 0.10 0.95 1.05 33.00 0.10 0.97 1.07 34.00 0.10 0.981.08 53.00 0.00 0.98 0.98 56.00 0.00 0.99 0.99

It is noted with respect to Table 4 that the greatest sum of sensitivityand specificity is for a heart rate threshold of 22 beats per minute forpredicting major adverse clinical events. FIG. 3B shows the ROC curvefor a heart rate threshold of 22 with respect to predicting a likelihoodof a major adverse clinical event. The area under the curve is 0.79 witha standard deviation of 0.11 and a p-value of 0.0024.

In general, in accordance with the indications provided by the data inTables 3 and 4 and in FIGS. 3A and 3B, a subject's heart rate ismonitored. The median (or mean, or maximum) heart rate over a timeduration of more than two hours and less than eight hours (e.g., greaterthan three hours, greater than four hours, greater than five hours, orgreater than six hours) is determined and is compared to the median (ormean, or maximum) heart rate over a similar time duration at a similarperiod of the day (e.g., at the same time of day) on at least oneprevious day (e.g., the previous day). In response to determining (a)that the median (or mean, or maximum) heart rate of the present daydiffers from that of the previous day by a threshold amount of more than15 beats per minute (e.g., more than 18 beats per minute, e.g., morethan 20 beats per minute), and/or less than 30 beats per minute, then analert is generated in order to indicate that an adverse clinical eventis likely to occur.

Table 5 (shown below) shows the results of the ROC curve analysis ofrespective maximum respiration rate thresholds (i.e., thresholds for thedifference between the maximum respiration rate and that of the previousday), with respect to determining the likelihood of a subject undergoinga major or a moderate clinical event.

TABLE 5 Respiration rate threshold Sum of (breaths per Sensitivity andminute) Sensitivity Specificity Specificity 0.00 1.00 0.00 1.00 0.501.00 0.05 1.05 1.00 1.00 0.06 1.06 1.50 1.00 0.24 1.24 2.00 1.00 0.261.26 3.00 1.00 0.43 1.43 3.50 1.00 0.58 1.58 4.00 1.00 0.59 1.59 5.001.00 0.70 1.70 6.00 0.69 0.76 1.46 6.50 0.54 0.83 1.37 6.75 0.54 0.851.39 7.00 0.46 0.85 1.31 7.50 0.38 0.89 1.28 8.00 0.31 0.89 1.20 9.000.23 0.92 1.15 10.00 0.23 0.92 1.16 12.00 0.23 0.93 1.16 16.00 0.23 0.971.20 18.00 0.15 0.97 1.13 19.00 0.08 0.98 1.06 24.00 0.00 0.98 0.9835.00 0.00 0.99 0.99

It is noted with respect to Table 5 that the greatest sum of sensitivityand specificity is for a respiration rate threshold of 5 breaths perminute, for predicting major and moderate adverse clinical events. FIG.3C shows the ROC curve for a respiration rate threshold of 5 withrespect to predicting a likelihood of either a major or a moderateadverse clinical event. The area under the curve is 0.84 with a standarddeviation of 0.04, and a p-value of 0.000049.

Table 6 (shown below) shows the results of the ROC curve analysis ofrespective respiration rate thresholds (i.e., thresholds for thedifference between the maximum respiration rate and that of the previousday), with respect to determining the likelihood of a subject undergoinga major clinical event.

TABLE 6 Respiration rate threshold Sum of (breaths per Sensitivity andminute) Sensitivity Specificity Specificity 0.00 1.00 0.00 1.00 0.501.00 0.05 1.05 1.00 1.00 0.06 1.06 1.50 1.00 0.23 1.23 2.00 1.00 0.261.26 3.00 1.00 0.42 1.42 3.50 1.00 0.57 1.57 4.00 1.00 0.58 1.58 5.001.00 0.69 1.69 6.00 0.73 0.76 1.49 6.50 0.55 0.83 1.37 6.75 0.55 0.841.39 7.00 0.55 0.85 1.40 7.50 0.45 0.89 1.35 8.00 0.36 0.89 1.26 9.000.27 0.92 1.19 10.00 0.27 0.93 1.20 12.00 0.27 0.93 1.21 16.00 0.27 0.971.24 18.00 0.18 0.98 1.16 19.00 0.09 0.98 1.07 24.00 0.00 0.98 0.9835.00 0.00 0.99 0.99

It is noted with respect to Table 6 that the greatest sum of sensitivityand specificity is for a respiration rate threshold of 5 breaths perminute for predicting major adverse clinical events. FIG. 3D shows theROC curve for a respiration rate threshold of 5 with respect topredicting a likelihood of a major adverse clinical event. The areaunder the curve is 0.85 with a standard deviation of 0.04, and a p-valueof 0.00012.

In general, in accordance with the indications provided by the data inTables 5 and 6 and in FIGS. 3C and 3D, a subject's respiration rate ismonitored. The median (or mean, or maximum) respiration rate over a timeduration of more than two hours and less than eight hours (e.g., greaterthan three hours, greater than four hours, greater than five hours, orgreater than six hours) is determined and is compared to the median (ormean, or maximum) respiration rate over a similar time duration at asimilar period of the day (e.g., at the same time of day) on at leastone previous day (e.g., the previous day). In response to determining(a) that the median (or mean, or maximum) respiration rate of thepresent day differs from that of the previous day by a threshold amountof more than 3 breaths per minute (e.g., more than 4 breaths perminute), and/or less than 10 breaths per minute (e.g., less than eight,or less than six breaths per minute), then an alert is generated inorder to indicate that an adverse clinical event is likely to occur.

For some applications, the techniques described herein are used incombination with the techniques described in one or more of thefollowing references, both of which are incorporated herein byreference:

-   U.S. Pat. No. 7,077,810 to Lange; and/or-   U.S. Pat. No. 7,314,451 to Halperin.

For example, for some applications, as is generally described in U.S.Pat. No. 7,077,810 to Lange, pattern analysis module 22 is configured topredict the onset of an asthma attack or a different clinical event,and/or monitor its severity and progression. Module 22 typicallyanalyzes changes in breathing rate and in breathing rate variabilitypatterns in combination to predict the onset of an asthma attack.Although breathing rate typically slightly increases prior to the onsetof an attack, this increase alone is not always a specific marker of theonset of an attack. Therefore, in order to more accurately predict theonset of an attack, and monitor the severity and progression of anattack, module 22 typically additionally analyzes changes in breathingrate variability patterns. For some applications, module 22 compares oneor more of the following patterns to respective baseline patterns, andinterprets a deviation from baseline as indicative of (a) the onset ofan attack, and/or (b) the severity of an attack in progress:

-   -   a slow trend breathing rate pattern. Module 22 interprets as        indicative of an approaching or progressing attack an increase        vs. baseline, for example, for generally healthy subjects, an        attenuation of the typical segmented, monotonic decline of        breathing rate typically over at least 1 hour, e.g., over at        least 2, 3, or 4 hours, or the transformation of this decline        into an increasing breathing rate pattern, depending on the        severity of the attack;    -   a breathing rate variability pattern. Module 22 interprets as        indicative of an approaching or progressing attack a decrease in        breathing rate variability. Such a decrease generally occurs as        the onset of an episode approaches, and intensifies with the        progression of shortness of breath during an attack;    -   a breathing duty-cycle pattern. Module 22 interprets a        substantial increase in the breathing duty-cycle as indicative        of an approaching or progressing attack. Breathing duty-cycle        patterns include, but are not limited to, inspirium time/total        breath cycle time, expirium time/total breath cycle time, and        (inspirium+expirium time)/total breath cycle time; and    -   interruptions in breathing pattern such as caused by coughs,        sleep disturbances, or waking. Module 22 quantifies these        events, and determines their relevance to prediction of        potential asthma attacks.

Reference is made to FIG. 4, which is a graph illustrating breathingrate patterns of a chronic asthma patient, and which is the same as FIG.4 of U.S. Pat. No. 7,077,810 to Lange. Breathing of the asthma patientwas monitored during sleep on several nights. The patient's breathingrate was averaged for each hour of sleep (excluding periods of rapid eyemovement (REM) sleep). During the first approximately two months thatthe patient was monitored, the patient did not experience any episodesof asthma. A line 100 is representative of a typical slow trendbreathing pattern recorded during this non-episodic period, and thusrepresents a baseline slow trend breathing rate pattern for thispatient. It should be noted that, unlike the monotonic decline inbreathing rate typically observed in non-asthmatic patients, thebaseline breathing rate pattern of the chronically asthmatic patient ofthe experiment reflects an initial decline in breathing rate during thefirst few hours of sleep, followed by a gradual increase in breathingrate throughout most of the rest of the night.

Lines 102 and 104 were recorded on two successive nights at theconclusion of the approximately two-month period, line 102 on the firstof these two nights, and line 104 on the second of these two nights. Thepatient experienced an episode of asthma during the second of thesenights. Lines 102 and 104 thus represent a pre-episodic slow trendbreathing rate pattern and an episodic slow trend breathing ratepattern, respectively. As can be seen in the graph, the patient'sbreathing rate was substantially elevated vs. baseline during all hoursof the pre-episodic night, and even further elevated vs. baseline duringthe episodic night.

Using techniques described herein, the pattern of line 102 is comparedwith the baseline pattern of line 100, in order to predict that thepatient may experience an asthmatic episode. The pattern of line 104 iscompared with the baseline pattern of line 100 in order to assess aprogression of the asthmatic episode.

In accordance with the data shown in FIG. 4, for some applications, asubject's respiration is detected on first and second days over similartime durations and at similar time periods (e.g., during the first two,three four, five, or six hours of the subject's sleep). A parameter ofthe subject's respiration based upon the detected respiration rate onthe second day is compared with that of the first day. An alert isgenerated in response to the comparison indicating that an adverseclinical event is approaching, e.g., in response to determining that thedifference between the median, the mean, and/or the maximum respirationrate on the second day and that of the first day exceeds a threshold.

For some applications, techniques as described in U.S. Pat. No.7,314,451 to Halperin are used in conjunction with the techniquesdescribed herein. For example, for some applications, system 10 monitorsand records patterns throughout all or a large portion of a night. Theresulting data set generally encompasses typical long-term respiratoryand heartbeat patterns, and facilitates comprehensive analysis.Additionally, such a large data set enables rejection of segmentscontaminated with movement or other artifacts, while retainingsufficient data for a statistically significant analysis.

Although breathing rate typically slightly increases prior to the onsetof an asthma attack (or a different adverse clinical event), thisincrease alone is not always a specific marker of the onset of anattack. Therefore, in order to more accurately predict the onset of anattack, and monitor the severity and progression of an attack, in anembodiment of the present invention, breathing pattern analysis module22 additionally analyzes changes in breathing rate variability patterns.For some applications, module 22 compares one or more of the followingpatterns to respective baseline patterns, and interprets a deviationfrom baseline as indicative of (a) the onset of an attack, and/or (b)the severity of an attack in progress:

-   -   a slow trend breathing rate pattern. Module 22 interprets as        indicative of an approaching or progressing attack an increase        vs. baseline, for example, for generally healthy subjects, an        attenuation of the typical segmented, monotonic decline of        breathing rate typically over at least 1 hour, e.g., over at        least 2, 3, or 4 hours, or the transformation of this decline        into an increasing breathing rate pattern, depending on the        severity of the attack;    -   a breathing rate pattern. Module 22 interprets as indicative of        an approaching or progressing attack an increase or lack of        decrease in breathing rate during the first several hours of        sleep, e.g., during the first 2, 3, or 4 hours of sleep.    -   a breathing rate variability pattern. Module 22 interprets a        decrease in breathing rate variability as indicative of an        approaching or progressing attack. Such a decrease generally        occurs as the onset of an episode approaches, and intensifies        with the progression of shortness of breath during an attack;    -   a breathing duty-cycle pattern. Module 22 interprets a        substantial increase in the breathing duty-cycle as indicative        of an approaching or progressing attack. Breathing duty-cycle        patterns include, but are not limited to, inspirium time/total        breath cycle time, expirium time/total breath cycle time, and        (inspirium+expirium time)/total breath cycle time;    -   a change in breathing rate pattern towards the end of night        sleep (typically between about 3:00 A.M. and about 6:00 A.M.);        and    -   interruptions in breathing pattern such as caused by coughs,        sleep disturbances, or waking. Module 22 quantifies these        events, and determines their relevance to prediction of        potential asthma attacks.

Pattern analysis modules 22 and 23 typically determine baseline patternsby analyzing breathing and/or heart rate patterns, respectively, of thesubject during non-symptomatic nights. Alternatively or additionally,modules 22 and 23 are programmed with baseline patterns based onpopulation averages. For some applications, such population averages aresegmented by characteristic traits such as age, height, weight, andgender.

Reference is again made to FIG. 4, which is a graph illustratingbreathing rate patterns of a chronic asthma patient, measured during anexperiment conducted in accordance with an embodiment of the presentinvention. Using techniques described herein, breathing pattern analysismodule 22 compares the pattern of line 102 with the baseline pattern ofline 100, in order to predict that the patient may experience anasthmatic episode. Module 22 compares the pattern of line 104 with thebaseline pattern of line 100 in order to assess a progression of theasthmatic episode.

For some applications of the present invention, the deviation frombaseline is defined as the cumulative deviation of the measured patternfrom the baseline pattern. A threshold indicative of a clinicalcondition is set equal to a certain number of standard errors (e.g., onestandard error). Alternatively or additionally, other measures ofdeviation between measured and baseline patterns are used, such ascorrelation coefficient, mean square error, maximal difference betweenthe patterns, and the area between the patterns. Further alternativelyor additionally, pattern analysis module 16 uses a weighted analysisemphasizing specific regions along the patterns, for example, by givingincreased weight (e.g., double weight) to an initial portion of sleep(e.g., the first two hours of sleep) or to specific hours, for exampleas morning approaches (e.g., the hours of 3:00-6:00 a.m.).

Reference is now made to FIGS. 5 and 6, which are graphs of exemplarybaseline and measured breathing rate and heart rate nighttime patterns,respectively, and which are generally similar to FIGS. 6 and 7 of U.S.Pat. No. 7,314,451 to Halperin, which is incorporated herein byreference. Lines 200 and 202 (FIGS. 5 and 6, respectively) representnormal baseline patterns in the absence of an asthma attack. The barsrepresent one standard error. Lines 204 and 206 (FIGS. 5 and 6,respectively) represent patterns during nights prior to an onset of anasthma attack. Detection of the change in pattern between lines 200 and202 and lines 204 and 206, respectively, enables the early prediction ofthe approaching asthma attack, or other approaching adverse clinicalevents.

For some applications of the present invention, pattern analysis module16 is configured to predict the onset of a clinical manifestation ofheart failure, and/or monitor its severity and progression. Module 16typically determines that an episode is imminent when the module detectsincreased breathing rate accompanied by increased heart rate, and/orwhen the monitored breathing and/or heartbeat patterns have specificcharacteristics that relate to heart failure, such as characteristicsthat are indicative of apnea, Cheyne-Stokes Respiration (CSR), and/orperiodic breathing.

In accordance with the data shown in FIG. 5, for some applications, asubject's respiration is detected on first and second days over similartime durations and at similar time periods (e.g., during the first two,three four, five, or six hours of the subject's sleep). A parameter ofthe subject's respiration based upon the detected respiration rate onthe second day is compared with that of the first day. An alert isgenerated in response to the comparison indicating that an adverseclinical event is approaching, e.g., in response to determining that thedifference between the median, the mean, and/or the maximum respirationrate on the second day and that of the first day exceeds a threshold.

In accordance with the data shown in FIG. 6, for some applications, asubject's heart rate is detected on first and second days over similartime durations and at similar time periods (e.g., during the first two,three, four, five, or six hours of the subject's sleep). A parameter ofthe subject's cardiac cycle based upon the detected heart rate on thesecond day is compared with that of the first day. An alert is generatedin response to the comparison indicating that an adverse clinical eventis approaching, e.g., in response to determining that the differencebetween the median, the mean, and/or the maximum heart rate on thesecond day and that of the first day exceeds a threshold.

In accordance with the data shown in FIGS. 5 and 6, for someapplications, a subject's respiration rate and heart rate are detectedon first and second days over similar time durations and at similar timeperiods (e.g., during the first two, three four, five, or six hours ofthe subject's sleep). A parameter of the subject's respiration basedupon the detected respiration rate on the second day is compared withthat of the first day, and a parameter of the subject's cardiac cyclebased upon the detected heart rate on the second day is compared withthat of the first day. An alert is generated in response to thecomparisons indicating that an adverse clinical event is approaching,e.g., in response to determining that (a) the difference between themedian, the mean, and/or the maximum respiration rate on the second dayand that of the first day exceeds a threshold, and/or (b) the differencebetween the median, the mean, and/or the maximum heart rate on thesecond day and that of the first day exceeds a threshold.

Reference is now made to FIG. 7, which is the same as FIG. 23 of U.S.Pat. No. 7,314,451 to Halperin, which is incorporated herein byreference. FIG. 7 is a graph of baseline and breathing rate nighttimepatterns, respectively, measured in accordance with some applications ofthe present invention. A line 400 represents a normal baseline patternin the absence of Cheyne-Stokes Respiration, and a line 402 represents apattern during a night during CSR. The bars represent one standarderror. In accordance with the data shown in FIG. 7, for someapplications, a subject's respiration is detected on first and seconddays over similar time durations and at similar time periods (e.g.,during the first two, three four, five, or six hours of the subject'ssleep). A parameter of the subject's respiration based upon the detectedrespiration rate on the second day is compared with that of the firstday. An alert is generated in response to the comparison indicating thatan adverse clinical event is approaching, e.g., in response todetermining that the difference between the median, the mean, and/or themaximum respiration rate on the second day and that of the first dayexceeds a threshold.

For some applications, techniques described herein are used inconjunction with techniques as are generally described in US2007/0118054 to Pinhas, which is incorporated herein by reference. Forexample, as is described with reference to FIG. 18 of US 2007/0118054 toPinhas, for some applications, system 10 is adapted to monitor multipleclinical parameters such as respiration rate, heart rate, coughoccurrence, body movement, deep inspirations, expiration/inspirationratio, of subject 12. Pattern analysis module 16 is adapted to analyzethe respective patterns in order to identify a change in the baselinepattern of the clinical parameters. In some cases, this change, a newbaseline that is significantly different from the previous baselineindicates, for example, a change in medication and provides thecaregiver or healthcare professional with feedback on the efficacy oftreatment.

For some applications, system 10 calculates the average respiration rateand heart rate for predefined time segments. Such time segments can beminutes, hours, or days. By analyzing the history of the patient thesystem can calculate the correlation of respiration rate and heart ratepatterns. When an onset of an asthma attack approaches the correlationof heart rate and respiration rate pattern shows a clear change. Foreach night the respiration rate and heart rate in sleep during the hoursof 11:00 pm to 6:00 am (or over a different time period) is averaged.For each date, a respiration vector of length N with the averagerespiration rate of the last N nights and a heart rate vector of lengthN with the average heart rate for the last N nights is defined. N istypically between 3 and 30, for example 10. The correlation coefficientof the heart rate vector and the respiration vector is calculated foreach date by system 10. A moving window of several days is used tocalculate correlation coefficient changes between the respiration andheart rate vectors. A steady correlation coefficient pattern over atleast several days is required to identify a significant change ofcorrelation coefficient from one time interval to another. A significantchange is defined as a change in the correlation coefficient level of amagnitude larger than the typical correlation coefficient variation inthe previous time interval, e.g., a change larger than 3 standarddeviations of the correlation coefficient signal in the previous timeinterval. System 10 identifies such a significant change as anindication of an approaching clinical event.

As described in US 2007/0118054 to Pinhas, for some applications, duringsleep, sleep stage is identified using techniques described therein. Insuch applications, system uses the identified sleep stages to moreeffectively monitor a clinical condition of the subject. Control unit 14analyzes the signal generated by motion sensor 30, and in responsethereto, (a) identifies a sleep stage of the subject (e.g., a slow-wavesleep stage, or an REM sleep stage), and (b) identifies a clinicalparameter of the subject (e.g., a heart rate or respiratory rate) in theidentified sleep stage. The control unit then compares the clinicalparameter to a baseline clinical parameter for the identified sleepstage, and generates an output in response thereto.

For example, in some applications, system 10 monitors a subject (e.g., asleeping child) for fever, e.g., by checking for an elevated heart rateor respiratory rate during slow-wave sleep. By analyzing the signal frommotion sensor 30, the control unit identifies that the sleep stage ofthe subject is indeed a slow-wave sleep stage, and further identifiesthe heart rate or respiratory rate. The identified heart rate orrespiratory rate is then compared to the baseline slow-wave-sleep-stageheart rate or respiratory rate. In response to the comparison, thecontrol unit identifies a likelihood that the subject has fever. If thecontrol unit determines that it is likely that the subject has fever(e.g., in response to the heart rate or respiratory rate being elevated,relative to the baseline), the control unit generates an output (e.g.,an alert).

In some applications, the control unit identifies the likelihood thatthe subject has fever in response to an increasing trend in respiratoryrate, e.g., over the course of a night. Using sleep-stage identificationas described hereinabove, the control unit discards data acquired duringREM sleep of the subject, since respiration rate may increase during REMsleep even in a healthy subject.

In one embodiment, system 10 discards any data while subject 12 showedsignificant restlessness. Thus for example, the first few minutes thepatient is in bed and is still tossing and turning, with his large bodymovements having significantly stronger signals than the cyclicrespiration pattern, are discarded from this analysis. Also, forexample, if the sensor signal shows a significant amount of bodymovement during a particular period of time during the night, thecontrol unit might not identify that the subject likely has fever, evenif the heart rate of the subject was elevated during the particularperiod of time. Since heart rate tends to rise during periods ofmovement, the control unit does not generate a fever alert, since theelevated heart rate might be a result of the movement, rather than aresult of the subject having fever.

Reference is now made to FIG. 8, which is a schematic illustration ofapparatus 258 for monitoring a subject 12, in accordance with someapplications of the present invention. In particular, apparatus 258monitors a clinical condition of a subject, in response to a statisticof a clinical parameter (e.g., heart rate or respiratory rate) during aparticular sleep stage (e.g., slow-wave sleep). For example, the controlunit may (i) identify a sleep stage of the subject, (ii) identify anaverage of the clinical parameter for the identified sleep stage, and(iii) monitor the clinical condition, by comparing the average to abaseline.

As shown in FIG. 8, a sensor 316 (e.g., motion sensor 30) monitorssubject 12, and generates a signal in response thereto. Generally, inthe present description, the term “motion sensor 30” is used to refer toa sensor that does not contact or view the subject or clothes thesubject is wearing, while the term “sensor 316” or “physiological sensor316) refers more generally to any type of sensor, e.g., a sensor thatincludes an electromyographic sensor and/or an imaging sensor. Thus, aphrase such as “sensor 316 (e.g., motion sensor 30)” should be construedto mean that the scope of the described invention includes the use ofany type of sensor, but specifically, a non-contact and non-viewingsensor may be used.

At each of a plurality of times (e.g., every two minutes, over a periodof ten minutes or more), the control unit analyzes the signal. Inresponse to the analyzing, the control unit ascertains, at anascertaining step 260, if the sleep stage of the subject is the givensleep stage. If the sleep stage is the given sleep stage, the controlunit then identifies, at an identifying step 262, the clinical parameterof the subject. (Steps 260 and 262 are thus part of a “data-gathering”process that is performed by the control unit.) The control unit thendecides, at a decision step 264, whether to continue identifying theparameter, i.e., whether more data points should be gathered. If thecontrol unit decides not to continue, the control unit then computes, ata computing step 266, a statistic of the clinical parameter for thegiven sleep stage over the plurality of times. For example, the controlunit may compute the average heart rate that the subject experiencedwhile in slow-wave sleep, over all or a portion of the night.

Following computing step 266, at a comparing step 268, the control unitcompares the statistic to a baseline value that is specific to the givensleep stage. For example, the baseline value may be a value of theclinical parameter exhibited during a sleeping session that precedes thepresent sleeping session, e.g., the subject's average slow-wave-sleepheart rate over a previous night. The control unit then identifies theclinical parameter exhibited during the present sleeping session, asdescribed hereinabove, and compares it to the baseline. Alternatively,the baseline value may be a value of the clinical parameter exhibited ata first time during the present sleeping session, e.g., the heart rateof the subject at the beginning of the night. The control unit thenidentifies a value of the clinical parameter exhibited at a second timeduring the sleeping session that follows the first time, e.g., the heartrate of the subject in the middle of the night, and compares theidentified value to the baseline. Alternatively, the baseline value maybe a typical value for a relevant segment of the general population.

In response to the comparing (e.g., in response to the averageslow-wave-sleep heart rate being significantly higher or lower than thebaseline), the control unit monitors, at a condition-monitoring step270, a clinical condition of the subject. In response to the monitoring,the control unit generates an output (e.g., a visual or audio output),at an output-generating step 272.

It is hypothesized by the inventors that averaging the parameter over aspecific sleep stage gives a better indication as to the health statusof the subject, relative to averaging over the entire night. Forexample, an average of heart rate over the entire night may reflect theeffects of motion or dreams, which may not be relevant to determiningthe subject's health status. By averaging the heart rate over slow-wavesleep, while ignoring the other sleep stages, these “artifacts” aregenerally “filtered out”.

Typically, the control unit is further configured to compute thebaseline value, by executing steps 260, 262, 264, and 266 at an earliertime. In other words, the control unit is configured to gather data, asdescribed hereinabove, during a first plurality of times, and to thenestablish the baseline value of the parameter by computing (at computingstep 266) a first statistic (e.g., average) of the parameter over thefirst plurality of times. The control unit then gathers data during asecond plurality of times, computes a second statistic of the parameter(at computing step 266), and then executes comparing step 268 bycomparing the second statistic to the first statistic.

In some applications, the process described above is executed formultiple sleep stages, the data from the multiple sleep stages beingused to monitor the clinical condition. That is, for each identifiedsleep stage, the average respiration rate, heart rate and other clinicalparameters are calculated. This data is compared to a baseline definedfor that subject for each identified sleep stage, in order to identifythe onset or progress of a clinical episode.

For some applications, for each night, for each hour (or for longerdurations of time, such as more than two hours) of sleep, counted fromthe onset of sleep, the average respiration rate, heart rate and otherclinical parameters are calculated. This data is compared to baseline(i.e., each of the averages is compared to a respective baseline) inorder to identify the onset or progress of a clinical episode.

For some applications, for each night, for each hour (or for longerdurations of time, such as more than two hours, as describedhereinabove), the average respiration rate, heart rate and otherclinical parameters are calculated. This data is compared to baseline inorder to identify the onset or progress of a clinical episode. Forexample, the average respiration rate in sleep during 2:00 AM-3:00 AM iscalculated and compared to baseline for that subject in order toidentify the onset or progress of a clinical episode.

Reference is now made to FIG. 9, which shows plots of data obtained frommotion sensor 30, in accordance with some applications of the presentinvention. Plot 42 shows the heartbeat-related component of aballistocardiographic (BCG) signal from sensor 30, in arbitrary units(AU), for a febrile subject, while plot 44 shows the heartbeat-relatedcomponent of a BCG signal for a healthy subject. (The larger-scalechanges in signal strength over the period of time shown in the plotsare generally not significant in the context of the description below,in that these changes simply reflect the proximity of the subject to thesensor. For example, the overall upward trend in signal strength in plot42 simply reflects the fact that the subject moved closer to the sensorover the period of time shown in plot 42, and is not a result of thesubject having a fever.)

In some applications, the clinical parameter that is identified by thecontrol unit and compared to the baseline is the left ventricularejection time (LVET) of the subject. For example, as shown in plots 42and 44, the LVET of the subject is lower for a febrile subject than fora healthy subject. In response to identifying that the LVET (or averageLVET) of the subject is lower than a baseline, the control unit mayidentify that it is likely that the subject has fever, and may generatean alert in response thereto. For extracting the heartbeat-relatedcomponent of the raw sensor signal, and for identifying the LVET,techniques described in (i) US 2014/0371635 to Shinar et al., and/or(ii) Alametsa et al., “Ballistocardiographic studies with accelerationand electromechanical film sensors”, Medical Engineering & Physics,2009, which are incorporated herein by reference, may be used.

Reference is now made to FIG. 10, which shows plots of data obtainedfrom motion sensor 30, in accordance with some applications of thepresent invention. Plot 46 shows the respiration-related component of aBCG signal from sensor 30, in arbitrary units, for a healthy subject,while plot 48 shows the respiration-related component of a BCG signalfor a febrile subject. The breathing of the febrile subject is labored,as evidenced by the double-peak respiration pattern. In someapplications, the control unit identifies the labored breathing, andidentifies the likelihood that the subject has fever in response to: (a)comparing the identified clinical parameter to the baseline, asdescribed hereinabove, and (b) identifying the labored breathing.

In some cases, eating within a given amount of time before going tosleep may have a detrimental effect on sleep and/or health of a subject.(For example, the health of a diabetic subject may suffer if thediabetic subject eats within a given amount of time before going tosleep.) In some applications, control unit 14 ascertains, in response toanalyzing a signal from a physiological sensor 316 (e.g., motion sensor30), that the monitored subject ate within a given amount of time beforegoing to sleep. In response to the ascertaining, the control unitgenerates an output signal (e.g., a visual and/or audio signal)indicative that the subject ate within the given amount of time. Forexample, the control unit may indicate to the subject, and/or thesubject's physician, that the subject should take greater care in thefuture not to eat so close to bedtime. In some applications, the outputsignal is communicated to a smartphone of the subject and/or to asmartphone of the subject's physician.

Reference is now made to FIG. 11, which shows plots of data obtainedfrom sensor 316, in accordance with some applications of the presentinvention. Plot 50 shows the heart-rate signal of a subject during asleeping session, after the subject ate within three hours of the startof the sleeping session. Plot 52 shows the heart-rate signal of the samesubject during a different sleeping session, after the subject did noteat within six hours of the start of the sleeping session. For eachplot, control unit 14 identified, in response to analyzing theheart-rate signal, a likelihood that the subject ate within a givenamount of time of going to sleep, and generated an output signal 56 inresponse thereto. That is, (i) for plot 50, the control unit generatedan output indicative that the subject likely ate within three hours ofgoing to sleep, and (ii) for plot 52, the control unit generated anoutput indicative that the subject likely did not eat within six hoursof going to sleep.

Typically, the control unit identifies the likelihood that the subjectate shortly before going to sleep by analyzing a portion of theheart-rate signal that is near the beginning of the sleeping session.This portion is marked as portion 54 a in plot 50, and as portion 54 bin plot 52. Portions 54 a and 54 b differ from one another as follows:

(a) In portion 54 a, the heart rate (HR) of the subject is elevated,relative to portion 54 b. The inventors hypothesize that the elevatedheart rate is due to the additional activity of the gastrointestinaltract in digesting the food that was consumed shortly before the startof the sleeping session.

(b) In portion 54 a, the heart rate of the subject is generallynon-increasing, whereas in portion 54 b, the heart rate is increasing.The inventors hypothesize that the increasing heart rate in portion 54 bis driven by the sympathetic nervous system, which tends to become moreactive as the sleeping session progresses. In portion 54 a, on the otherhand, the level of gastrointestinal activity of the subject, which isrelatively high at the beginning of the sleeping session, decreases asthe sleeping session progresses. This decrease in gastrointestinalactivity, which has the effect of lowering the heart rate of thesubject, counteracts the increase in sympathetic nervous activity withrespect to heart rate; thus, the heart rate of the subject remainsgenerally constant.

In response to at least one of the differences above, the control unitmay identify the likelihood that the subject ate shortly before going tosleep. For example:

(a) The control unit may identify a relatively high likelihood that thesubject ate, by determining that the heart rate is greater than abaseline heart rate. (The baseline is typically subject-specific, and istypically learned by the control unit.) For example, the heart rate ofaround 65 beats per minute (BPM) in portion 54 a is greater than 52-58BPM, which, from portion 54 b, appears to be a reasonable baseline heartrate for the subject.

(b) Alternatively or additionally, the control unit may identify arelatively high likelihood that the subject ate, by determining that theheart rate of the subject does not increase over a particular intervalby more than a threshold.

In one embodiment, pattern analysis module 16 is adapted to identifypreterm labor in a pregnant woman. Preterm labor is the leading cause ofperinatal morbidity and mortality in the United States. Early diagnosisof preterm labor enables effective tocolytic therapy to prevent fulllabor. In one embodiment, system 10 is adapted to identify themechanical signal of contractions. In one embodiment, motion sensor 30is adapted to include multiple sensors located in the vicinity of thelegs, pelvis, lower abdomen, and upper abdomen. Pattern analysis module16 identifies a mechanical signal that is strongest in the area of thelower abdomen and pelvis and weaker in the upper abdomen as a signalindicative of contractions. In one embodiment, system 10 is adapted todifferentiate between Braxton Hicks contractions and normal contractionsin order to minimize false alarms of preterm labor. In one embodiment,differentiation between regular contractions and Braxton Hickscontractions is done by comparing the frequency and strength of thecontractions. In one embodiment, the strength of the contractionmechanical signal is normalized by the strength of the rhythmic heartand respiration signals. In one embodiment, the system logs thecontractions and alerts the subject or a clinician upon having thenumber or hourly rate of contractions exceed a predefined threshold.

Normal breathing patterns in sleep are likely to be subject to slowchanges over days, weeks, months and years. Some changes are periodicdue to periodic environmental changes like change in seasons, or to aperiodic schedule such as a weekly schedule (for example outdoor playevery Saturday), or biological cycles such as the menstrual cycle. Otherchanges might be monotonically progressive—for example, changes due tochildren growing up or adults aging. It is desirable to track these slowchanges dynamically via an adaptive system.

In an embodiment of the present invention, system 10 is adapted tomonitor parameters of the patient including breathing rate, heart rate,coughing counts, expiration/inspiration ratios, augmented breaths, deepinspirations, tremor, sleep cycle, and restlessness patterns, amongother parameters. These parameters are defined herein as “clinicalparameters.”

In an embodiment of the present invention, pattern analysis module 16combines clinical parameter data generated from one or more of analysismodules 22, 23, 26, 28, 29, and analyzes the data in order to predictand/or monitor a clinical event. For some applications, pattern analysismodule 16 derives a score for each parameter based on the parameter'sdeviation from baseline values (either for the specific patient or basedon population averages). Pattern analysis module 16 may combine thescores, such as by taking an average, maximum, standard deviation, orother function of the scores. The combined score is compared to one ormore threshold values (which may be predetermined) to determine whetheran episode is predicted, currently occurring, or neither predicted noroccurring, and/or to monitor the severity and progression of anoccurring episode. For some applications, pattern analysis module 16learns the criteria and/or functions for combining the individualparameter scores for the specific patient or patient group based onpersonal history. For example, pattern analysis module 16 may performsuch learning by analyzing parameters measured prior to previousclinical events.

In one aspect, pattern analysis module 16 is adapted to analyze therespective patterns, for example, the patterns of slow changes mentionedabove, in order to identify a change in baseline characteristic of theclinical parameters. For example, in order to identify the slow changein average respiration rate in sleep for a child due to growing up, amonthly average of the respiration rate in sleep is calculated. System10 then calculates the rate of change in average respiration rate fromone month to the next and displays that to the patient or healthcareprofessional.

In some applications, the control unit identifies a baseline valuecorresponding to a day of the week, and derives a score based on adeviation of the identified clinical parameter from the day-specificbaseline value. For example, as noted above, breathing patterns in sleepmay change over a weekly schedule of the subject; for example, physicalactivity of the subject may generally recur on a particular day of theweek. The control unit identifies the baseline value in response to theweekly schedule. For example, in response to recurring outdoor play on aparticular day of the week, the control unit may identify abreathing-rate baseline value corresponding to the particular day of theweek that is greater than the baseline value for other days of the week.(It is noted that a baseline value may be said to correspond to aparticular day of the week if it corresponds to the sleeping period atthe end of, or immediately following, the particular day of the week.For example, the baseline value identified for a subject who went tosleep at 1:00 a.m. Monday would typically be the baseline value thatcorresponds to Sunday.) Additionally or alternatively, system 10identifies a first baseline value corresponding to a weekend day, and asecond baseline value, which is different from the first baseline value,corresponding to a weekday. For example, system 10 may identify that theaverage respiration rate in sleep during weekends is higher than onweekdays, and may therefore use in weekends a different (i.e., higher)baseline for comparison and decision on whether a clinical episode ispresent or oncoming.

As noted above, in some applications, the baseline value is identifiedin response to environmental changes. For example, as noted above, thebaseline value may be identified in response to a change in seasons.Alternatively or additionally, the baseline value may be identified inresponse to changes in room-environment parameters, such as roomtemperature and humidity. In some applications, system 10 comprises atemperature sensor 106 (FIG. 8) configured to detect a room temperatureand/or an in-bed temperature (i.e., the temperature of the“microclimate” underneath the subject's sheets), and the baseline valueis identified in response to the room temperature and/or in-bedtemperature. For example, the control unit may identify a higherheart-rate baseline value for a higher room temperature and/or a higherin-bed temperature, relative to a lower room temperature and/or a lowerin-bed temperature, since the heart rate of the subject is expected toincrease with an increase in ambient temperature.

In one embodiment, system 10 monitors and logs the clinical condition ofa patient over an extended period of time. During the same period oftime, behavioral patterns, treatment practices and external parametersthat may be affecting the patient's condition are monitored and loggedas well. This information is input into system 10. System 10 calculatesa score for the clinical condition of the patient based on the measuredclinical parameters.

In general, control unit 14 may be embodied as a single control unit 14,or a cooperatively networked or clustered set of control units. Controlunit 14 is typically a programmed digital computing device comprising acentral processing unit (CPU), random access memory (RAM), non-volatilesecondary storage, such as a hard drive or CD ROM drive, networkinterfaces, and/or peripheral devices. Program code, including softwareprograms, and data are loaded into the RAM for execution and processingby the CPU and results are generated for display, output, transmittal,or storage, as is known in the art. Typically, control unit 14 isconnected to one or more sensors via one or more wired or wirelessconnections. Control unit 14 is typically configured to receive signals(e.g., motions signals) from the one or more sensors, and to processthese signals as described herein. In the context of the claims andspecification of the present application, the term “motion signal” isused to denote any signal that is generated by a sensor, upon the sensorsensing motion. Such motion may include, for example, respiratorymotion, cardiac motion, or other body motion, e.g., large body-movement.Similarly, the term “motion sensor” is used to denote any sensor thatsenses motion, including the types of motion delineated above.

Techniques described herein may be practiced in combination withtechniques described in one or more of the following patents and patentapplications, which are incorporated herein by reference. In someapplications, techniques and apparatus described in one or more of thefollowing applications are combined with techniques and apparatusdescribed herein:

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It will be appreciated by persons skilled in the art that the presentinvention is not limited to what has been particularly shown anddescribed hereinabove. Rather, the scope of the present inventionincludes both combinations and subcombinations of the various featuresdescribed hereinabove, as well as variations and modifications thereofthat are not in the prior art, which would occur to persons skilled inthe art upon reading the foregoing description.

1. (canceled)
 2. Apparatus comprising: a sensor configured to monitor asubject during a sleeping session of the subject, and to generate asensor signal in response thereto; a control unit configured to: analyzethe sensor signal; in response to analyzing the sensor signal, performan action selected from the group consisting of: identifying that thesubject is currently undergoing an apnea episode, and predicting thatthe subject is going to undergo an apnea episode; and in responsethereto, change a position of at least a portion of a body of thesubject by activating a device.
 3. The apparatus according to claim 2,wherein the control unit is configured to determine a posture of thesubject by analyzing the sensor signal.
 4. The apparatus according toclaim 2, wherein the device includes an inflatable pillow, and whereinthe control unit is configured to change the position of at least theportion of the subject's body by changing a level of inflation of thepillow.
 5. The apparatus according to claim 2, wherein the deviceincludes a bed, and wherein the control unit is configured to change theposition of at least the portion of the subject's body by changing anangle of at least a portion of the bed.
 6. The apparatus according toclaim 2, wherein the device includes a mattress, and wherein the controlunit is configured to change the position of at least the portion of thesubject's body by changing an angle of at least a portion of themattress.
 7. The apparatus according to claim 2, wherein, in response toanalyzing the sensor signal, the control unit is configured to identifythat the subject is currently undergoing an apnea episode, and, inresponse thereto, to change the position of at least the portion of thesubject's body by activating a device.
 8. The apparatus according toclaim 2, wherein, in response to analyzing the sensor signal, thecontrol unit is configured to predict that the subject is going toundergo an apnea episode, and, in response thereto, to change theposition of at least the portion of the subject's body by activating adevice.